The present invention, in some embodiments thereof, relates to mode selection for MIMO in wireless communication and, more particularly, but not exclusively, to such mode selection carried out at a mobile station.
MIMO or multiple input multiple output, is a form of smart antenna technology for radio communication. Herein it is considered in relation to radio networks involving base stations and mobile stations, where typically the base station has multiple antennas, say up to four, and the mobile station has two antennas. In particular it is considered in relation to networks conforming to the IEEE 802.16 standard/WiMAX and/or 3GPP/LTE and/or 3GPP2/UMB that employ MIMO-OFDM signals in their downlink channel.
In a MIMO system, as shown in FIG. 1 a transmitter Tx 10 sends a signal using two antennas. The signal is received by two antennas at receiver Rx 12. The MIMO system is schematized in FIG. 2, in which a transmission source 14 sends multiple streams using multiple transmit antennas 1, 2 and 3. The transmit streams go through a matrix channel which consists of multiple paths between multiple transmit antennas at the transmitter 14 and multiple receive antennas 1, 2, and 3 at receiver 16. The receiver 16 obtains the received signal vectors from the multiple receive antennas and decodes the received signal vectors into the original information. The MIMO system may be modelled by an equation of the form:y=Hx+n 
where y and x are the receive and transmit vectors, respectively. In addition, H and n are the channel matrix and the noise vector, respectively.
Referring to information theory, the average capacity of a MIMO system is as follows:
Closed loop MIMO can achieve
      C    CL    =            E      [                        max          Q                ⁢                              log            2                    ⁢                      det            ⁡                          (                              I                +                                  HQH                  H                                            )                                          ]        =          E      ⁡              [                              log            2                    ⁢                      det            ⁡                          (                              I                +                                  USU                  H                                            )                                      ]            
where UDVH=svd(H) and S=waterfilling(D2). The functions of svd( ) and waterfilling( ) in turn represent singular value decomposition and power allocation by the water filling rule, respectively.
Open loop MIMO can achieve
      C    OL    =                    max        Q            ⁢              E        ⁡                  [                                    log              2                        ⁢                          det              ⁡                              (                                  I                  +                                      HQH                    H                                                  )                                              ]                      =          E      ⁡              [                              log            2                    ⁢                      det            ⁡                          (                              I                +                                  HH                  H                                            )                                      ]            
since any unitary matrix of Q can achieve the capacity of an open-loop MIMO system, which is mostly min(Nt,Nr) times larger than that of a SISO system.
Currently IEEE 802.16e and the WimaX-Forum system profile mandate two MIMO schemes for data transmission. The first one is the Alamouti scheme (also know as matrix A) which tries to maximize the transmission diversity without increasing spectral efficiency. In other words Matrix A takes the advantage of the number of antennas and the different routes between the antennas to send a single signal, using spatial diversity to assist with decoding at the far end to overcome noise etc. The second mode is Spatial Multiplexing (SM) (also known as matrix B). Spatial Multiplexing splits or multiplexes the signal between the different channels so that each channel is transmitting a different part of the signal. Matrix B mode aims at doubling the transmission spectral efficiency in terms of bits/sub-carrier towards the said MS, by sacrificing diversity (i.e., Matrix B does not provide any diversity).
The question arises as to which of the above modes, Matrix A or Matrix B to select in any given multipath fading channel situation. In general matrix A is more efficient since the spatial diversity overcomes channel problems such as noise and interference. In low noise, low interference situations, however, spatial diversity has nothing to overcome, so that more data can be sent if Matrix B is used.
In general the process of MIMO mode selection for downlink transmissions is carried out at the mobile station (or hand-held device). However, the desired MIMO mode depends on the MIMO channel state as well as the interference environment seen at the receiver side. Thus a further question remains as to whether the task of MIMO mode selection should remain with the receiver side (MS) or move to the sender side (BS). In the WiMAX forum for example it was agreed that the MIMO mode selection is made at the MS side since it is attributed with better knowledge of the channel and interference level as perceived at its two receive antennas. Thus, a challenge is to provide an apparatus and method to enable MIMO mode selection at the MS side that takes into account the channel and instantaneous interference level. Such an apparatus may be applicable to base stations and to 802.16 systems. Although such a mode selection algorithm may be implemented at the mobile device side, it may be expected to have a major impact on the system and the BS performance, and in particular on the link adaptation process and scheduling process running at the BS side. Currently, MIMO mode selection is a mandatory feature at the WiMAX-Forum system profile and it may also be a mandatory feature in 802.16m and LTE.
Currently, the task of MIMO mode selection is an implementation specific algorithm carried out by the specific mobile station without the BS being aware of what the individual mobile station is doing, hence the current failure to deliver advantages to the network.
Current methods involve obtaining the channel conditions using pilot tones. A channel estimate is made using linear mean square error techniques (LMMSE) or maximum likelihood, of which the latter gives the better tradeoff.
Some recent proposals use the estimate of the MIMO channel matrix H (denoted as Ĥ), and apply singular value decomposition (SVD) to the matrix Ĥ to obtain the channel singular values. Since in OFDM/OFDMA the signal consists of a sum of subcarriers, we actually refer to a MIMO channel per sub-carrier Ĥk where Ĥk has dimensions of M×N. M is the number of transmit antennas and N is the number of receive antennas.
Thus in practice we consider an ensemble {Ĥk}k=1K where K is the total available sub-carriers. In practice, cellular systems contain pilot tones that are introduced for the purpose of synchronization and estimation and data tones.
Hence mobile station receivers may choose to work on a reduced set of pilot tones only in order to simplify receiver implementation and thus the available ensemble is {Ĥk}k=1P where P⊂K.
Having {Ĥk}k=1K the receiver computes for each Ĥk its singular values and its condition number defined as
      λ    min        λ    max  where λmin, λmax denote the minimal and maximal singular values. Matrix B is considered whenever the condition number is close to unity. As an example, we consider the WiMax standard that deals with 2×2 MIMO channels only. In this case per each subcarrier, Ĥk has a 2×2 dimension.
Per each Ĥk we perform singular value decomposition (SVD) defined as:
SVD(Ĥk)=USV where S is a diagonal matrix whose elements on the main diagonal are the singular values of Ĥk. These singular values are the square root of the channel eigenvalues obtained from ĤkHĤk. Thus, eig(ĤkHĤk)=[λ1,k2,λ2,k2] and on each tone the matrix S (for the case of MIMO 2×2) can be written as
  S  =            [                                                  λ                              1                ,                k                                                          0                                                0                                              λ                              2                ,                k                                                        ]        .  For each tone we compute its condition number denoted as
      γ    k    =                    λ                  1          ,          k                            λ                  2          ,          k                      .  
The condition number has a one to one correspondence to the k-th tone MIMO channel rank. To summarize, the decision on a single stream (e.g., matrix A) or multi stream (e.g. Matrix B) is based on the broadband MIMO channel rank as computed by the mobile station. The condition number is an indication of the relative strengths of the channel Eigenvalues but it fails to take into account issues of interference and fading. That is to say, there may be considerable interference in the channel, regular fading etc. for which the A matrix is still required, but a selection based on the condition number fails to take these factors into account and tries to select the B matrix. Cellular systems are usually interference limited and this interference is spatially and temporally colored. As a consequence, MIMO mode decisions that are based on the channel's rank and statistics may lead to erroneous decisions and overall bad system performance.
The present embodiments seek to overcome the problems of opaque mobile station-based MIMO mode selection, and of inadvisable selection of the B matrix even under conditions of interference and channel fading.